Skip to content

Instantly share code, notes, and snippets.

@iamwildtuna
iamwildtuna / gist:7772b7c84a11bf6e1385f23096a73a15
Last active April 22, 2026 07:02
VPN IP Addresses (IP адреса ChatGPT, Copilot, Meta, Facebook, Instagram, YouTube, Medium, X ex. Twitter, Discord)
Meta (Instagram, Facebook)
// Узлы
157.240.253.174, 157.240.253.172, 157.240.253.167, 157.240.253.63, 157.240.253.32
157.240.252.174, 157.240.252.172, 157.240.252.167, 157.240.252.63, 157.240.252.38
57.144.112.34, 57.144.110.1, 157.240.205.174, 87.245.223.97
// Подсети
213.102.128.0/24
204.15.20.0/22
199.201.0.0/16
@githubfoam
githubfoam / xdr cheat sheet
Last active April 22, 2026 07:02
xdr cheat sheet
#======================================================================
To make an informed decision on purchasing an XDR product, you need a structured approach to evaluating vendors and ensuring the solution meets your security needs. Here's how you can organize the process effectively:
1. Define Evaluation Goals & Success Criteria
Establish clear objectives for adopting an XDR solution (e.g., better threat detection, improved response automation).
Identify key security gaps that need addressing.
Define measurable success criteria for the evaluation (e.g., ease of integration, accuracy of threat detection, response time).
@retlehs
retlehs / backlinks.sh
Created April 17, 2026 15:54
Backlinks for any domain via Common Crawl

Boris Cherny’s 10 team-sourced tips for using Claude Code

See Boris’s post: these tips come from the Claude Code team, and there’s no single “right” setup—experiment and keep what works.

1) Do more in parallel

  • Run 3–5 Claude sessions at once, one per task.
  • The team’s preferred approach is git worktree so each session has its own isolated working directory.
  • Some folks also keep a dedicated “analysis” worktree for log reading / BigQuery-style investigation.

2) Start complex tasks in plan mode

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@WildSiphon
WildSiphon / DockerDesktop.yaml
Created July 5, 2025 11:00
Docker Desktop direct download links
4.0.0:
Windows: https://desktop.docker.com/win/main/amd64/67817/Docker%20Desktop%20Installer.exe
Mac with Intel chip: https://desktop.docker.com/mac/main/amd64/67817/Docker.dmg
Mac with Apple chip: https://desktop.docker.com/mac/main/arm64/67817/Docker.dmg
release_date: '2021-08-31'
4.0.1:
Windows: https://desktop.docker.com/win/main/amd64/68347/Docker%20Desktop%20Installer.exe
Mac with Intel chip: https://desktop.docker.com/mac/main/amd64/68347/Docker.dmg
Mac with Apple chip: https://desktop.docker.com/mac/main/arm64/68347/Docker.dmg
release_date: '2021-09-13'

🚀 Team Playbook — Frontend (Next.js) V3.3

Workspace: local-frontend
เครื่องมือ: VS Code + GitHub Copilot + claude-context MCP
Stack: Next.js 14+ (App Router) + TypeScript + Tailwind CSS + Redux Toolkit + next-intl

📘 ไฟล์นี้ครอบคลุมงานฝั่ง Frontend ทั้งหมด
สำหรับงาน Backend → ดู Team_Playbook_BE.md
สำหรับ Full-Stack Feature → ต้องทำ หมวด 0: API Contract ก่อนเสมอ